Genome and Phenome to Define Disease Risk with Antinuclear Antibodies

项目摘要 抗核抗体（ANA）是针对自身抗原的抗体，通常用于帮助 诊断系统性红斑狼疮（SLE）。因为几乎所有患者的检测结果均为阳性（ANA+） SLE患者-甚至在疾病发作前几年-ANA检测阳性几乎被认为是SLE的必要条件， SLE的诊断然而，该测试在大部分普通人群中也呈阳性（约20%）。 虽然这些ANA+个体中很少会在未来发展为自身免疫性疾病，但临床上， 抗核抗体阳性对无自身免疫性疾病患者的影响尚不清楚。第二个问题是， ANA+在没有自身免疫性疾病的人中的常见发生可导致不正确的免疫应答的问题。 SLE的诊断，特别是如果ANA+的人也有关节或肌肉疼痛。为了更准确地诊断 SLE和防止误诊，我们需要解决两个主要的知识差距：1）我们需要了解 抗核抗体检测阳性对没有自身免疫性疾病的人的重要性; 2）我们需要能够 预测哪些ANA检测阳性的人患有或将发展为SLE。 在这项研究中，我们将评估总体假设，即临床和遗传信息可以：1） 明确ANA阳性在无自身免疫性疾病人群中的临床后果，以及2） 改善风险预测，以区分SLE风险增加的人群。因此，我们提出了三个 具体目的：1）检验假设，即在没有自身免疫性疾病的人中，ANA阳性与 与临床表型（使用临床和遗传方法）; 2）检验临床表型与遗传学相关的假设， 预测模型将准确区分早期SLE患者或SLE风险患者， ANA阳性而无自身免疫性疾病的患者;以及3）检验以下假设： 遗传和临床信息将准确区分SLE风险患者。为了实现这些目标，我们 将使用范德比尔特大学医学中心生物银行（BioVU）和去识别的电子健康记录 (EHR)使用最先进的遗传技术和数据驱动的 预测工具。 这些研究的结果可以：a）确定ANA阳性且无自身免疫性疾病的人是否 改变了患病风险，可用于指导卫生保健决策;以及B）改变 通过提高早期SLE诊断的准确性，识别未来风险最高的患者。这些 研究结果将有助于临床医生更早地开始治疗，以控制炎症和防止损伤， 降低误诊率，从而保护患者免受不必要的治疗， 方面的影响.

PROJECT SUMMARY Antinuclear antibodies (ANA) are antibodies that react against self-antigens and are commonly used to help diagnose systemic lupus erythematosus (SLE). Because the test is positive (ANA+) in almost every patient with SLE— even years before the disease onset—a positive ANA test is considered virtually a requisite for the diagnosis of SLE. However, the test is also positive in a large proportion of the general population (~20%). Although very few of these ANA+ individuals will develop an autoimmune disease in the future, the clinical impact of a positive ANA in people without autoimmune disease is unknown. A second problem is that the common occurrence of ANA+ in people without autoimmune disease can lead to the problem of an incorrect diagnosis of SLE, particularly if an ANA+ person also has joint or muscle pain. To more accurately diagnose SLE and prevent false diagnoses, we need to address two major knowledge gaps: 1) we need to understand the importance of a positive ANA test in people without an autoimmune disease; and 2) we need to be able to predict which people with a positive ANA test have or will develop SLE. In this study we will evaluate the overarching hypothesis that clinical and genetic information can: 1) define the clinical consequences of positive ANA in people without autoimmune diseases, and 2) improve risk prediction to differentiate people with increased risk of SLE. Thus, we proposed three Specific Aims: 1) test the hypothesis that a positive ANA in people without autoimmune disease is associated with clinical phenotypes (using a clinical and a genetic approach); 2) test the hypothesis that a clinical prediction model will accurately discriminate patients with early SLE or who are at risk for SLE from among those with positive ANA without an autoimmune disease; and 3) test the hypothesis that the combination of genetic and clinical information will accurately discriminate patients at risk for SLE. To address these aims, we will use the Vanderbilt University Medical Center Biobank (BioVU) and de-identified electronic health records (EHR) to create genetic and clinical risk scores using state-of-the-art genetic techniques and data-driven prediction tools. The results of these studies could: a) define whether people with a positive ANA and no autoimmune disease have an altered risk of illnesses that could be used to guide health-care decisions; and b) transform the care of SLE by improving the accuracy of early-stage SLE diagnosis and identify patients at highest future risk. These findings will help clinicians start treatment earlier to control inflammation and prevent damage and will decrease the rates of misdiagnosis, thereby protecting patients from unnecessary therapies and their side effects.